Ekkono
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About
Ekkono is a specialized Edge AI software company providing a toolkit for developers to build and deploy machine learning models directly onto resource-constrained devices. Unlike many AI platforms that rely on cloud processing or edge inference only, Ekkono enables incremental learning on the device itself. This allows every individual unit—whether a pump, motor, or vehicle—to learn and adapt to its specific operational environment in real-time, facilitating genuine individual condition monitoring and proactive maintenance. The platform consists of a Software Development Kit (SDK) and Ekkono Synthesis. The SDK supports a comprehensive workflow that bridges the gap between data scientists, application engineers, and embedded software developers. It features C and C++ libraries with a very small memory footprint, no external dependencies, and hotswappable ML models. Synthesis provides model life-cycle management, aggregating individual learnings through federated learning, model correlation, and outlier detection to improve the collective intelligence of a fleet. Ekkono is specifically optimized for real-world sensor data such as vibration, pressure, and temperature, making it ideal for the automotive, industrial automation, and HVAC industries. Major companies like Volvo, Husqvarna, and Siemens use Ekkono to transition from reactive maintenance to proactive, adaptive automation. Because the software is hardware and sensor agnostic, it can be integrated into existing products without requiring significant architectural changes or high-power GPUs. What truly distinguishes Ekkono from open-source alternatives like TensorFlow Lite is its focus on incremental learning and sensor-based anomaly detection rather than just image classification. While open-source tools often lack dedicated commercial support and require massive datasets for initial training, Ekkono is purpose-built for industrial deployment where real-time preprocessing and on-device training are critical. This ensures that models remain accurate even as machine components wear down or environmental conditions shift.
Pros & Cons
Supports on-device training and incremental learning rather than just inference.
Extremely small memory footprint suitable for resource-constrained embedded devices.
Hardware and sensor agnostic, allowing deployment across diverse product lines.
Provides dedicated commercial support unlike many open-source alternatives.
Optimized specifically for industrial sensor data like pressure and temperature.
Not intended for deep learning tasks like complex image or video classification.
Pricing is not transparent and requires a consultation for a customized quote.
Requires specialized knowledge of C and C++ for successful embedded integration.
Use Cases
Embedded software engineers can deploy ML models to industrial pumps to monitor vibration and temperature for predictive maintenance.
Automotive manufacturers can implement real-time individual condition monitoring for vehicle components to adapt to different driving styles.
HVAC system developers can use adaptive automation to learn optimal power consumption patterns based on specific building environments.
Platform
Task
Features
• federated learning
• hardware agnostic
• incremental learning
• hotswappable models
• sensor data preprocessing
• model life-cycle management
• c and c++ libraries
• small memory footprint
FAQs
How is Ekkono different from TensorFlow Lite?
TensorFlow Lite is primarily designed for deep learning tasks like image classification, whereas Ekkono is optimized for sensor data like pressure and temperature. Ekkono also supports incremental learning at the edge, a feature often missing in Lite versions.
What programming languages does Ekkono support?
Ekkono provides C and C++ libraries designed to run in any environment on any hardware. This makes it highly versatile for embedded software engineers working with resource-constrained systems.
Can Ekkono models be updated while the device is running?
Yes, Ekkono features hotswappable ML models, allowing for updates without interrupting the device's operations. This is part of their Model Life-Cycle Management (MLCM) approach for production environments.
Does Ekkono require cloud connectivity to learn?
No, Ekkono is designed to perform both inference and training directly at the edge on the individual unit. While collective learning can be managed via Ekkono Synthesis, individual condition monitoring happens locally.
Pricing Plans
Custom
Unknown Price• Technical walk through
• 1-on-1 demo
• Edge AI implementation
• Model life-cycle management
• Commercial support
• On-device training
• Ekkono Synthesis access
• Hardware agnostic libraries
Job Opportunities
There are currently no job postings for this AI tool.
Ratings & Reviews
No ratings available yet. Be the first to rate this tool!
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